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Epigenetic Regulation

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Updated: May 3, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Predicting DNA methylation level across human tissues.

Baoshan Ma¹, Elissa H Wilker, Saffron A G Willis-Owen

¹Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA, College of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning Province 116026, China, Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA, National Heart and Lung Institute, Imperial College, London SW3 6LY, UK, Department of Clinical Sciences and Community, University of Milan, Milan 20122, Italy, Division of Cardiac Surgery, Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA and Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.

Nucleic Acids Research

|January 22, 2014

Summary

This summary is machine-generated.

Scientists developed a new statistical model to predict DNA methylation in specific tissues using data from surrogate tissues. This advance aids epigenetic research in complex diseases and non-invasive screening.

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Methyl-binding DNA capture Sequencing for Patient Tissues

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Published on: October 31, 2016

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Area of Science:

Epigenetics and Genomics
Computational Biology
Disease Biomarkers

Background:

Tissue-specific DNA methylation patterns are crucial for cell differentiation and understanding complex diseases.
Epigenetic variations across tissues present challenges for disease research, particularly when target tissues are difficult to access.

Purpose of the Study:

To develop and validate a novel statistical model for predicting locus-specific DNA methylation in a target tissue using methylation data from a surrogate tissue.
To enhance the accuracy of cross-tissue methylation prediction for epidemiological studies.

Main Methods:

Developed a statistical model to predict methylation in target tissues from surrogate tissues.
Evaluated the model using publicly available data and two IlluminaBeadChip studies (childhood asthma and postoperative atrial fibrillation).
Validated predictions using peripheral blood leukocytes (PBL), lymphoblastoid cell lines, atrium, and artery tissues.

Main Results:

The novel method significantly improved the accuracy of cross-tissue methylation prediction, with R(2) values increasing substantially (e.g., PBL-to-artery from 0.38 to 0.89).
An extended model incorporating multiple CpGs further enhanced prediction performance.
Cross-validation and cross-study predictions confirmed the model's robustness.

Conclusions:

Locus-specific methylation differences between tissues are highly consistent across individuals.
The developed model enables improved epigenetic analysis in hard-to-access tissues using readily available surrogate tissues like blood.
This approach holds potential for recalibrating large-scale epidemiological studies and enabling non-invasive disease screening via epigenetic profiles.